Abstract: A higher-dimensional-object-function particle swarm optimizer HDOF-PSO algorithm is proposed for the prematurities which are easy to take place when dealing with the higher-dimensional object function by BPSO(basical particle swarm optimizer) algorithm. The reason why HDOF-PSO is difficult to be deal with hasical PSO algorithm is analyzal. The confidence level and trial-and-error strategy are introduced into the algorithm to accelerate its convergence rate with the probability of success also introduced in to enable the adaptive correction available to the probability of mutation in searching process. The experimental results of a specific set of benchmerk functions showed that the HDOF-PSO algorithm has better convergence and higher dimensional object functions.